ISO/IEC 16466:2025
(Main)Information technology — 3D printing and scanning — Assessment methods of 3D scanned data use in 3D printing
Information technology — 3D printing and scanning — Assessment methods of 3D scanned data use in 3D printing
This document specifies methods and metrics for assessing the accuracy and precision of 3D scanned data for use in 3D printing, throughout the full 3D printing lifecycle. This document focuses mainly on 3D scanned data from computed tomography. Computed tomography can acquire information concerning the internal structures, regional density, orientation and/or alignment of scanning objects, as well as their shape and appearance. This document is applicable to the assessment of image-based modelling, segmentation, and 3D models. This document is not intended to assess the 3D printed product itself.
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Buy Standard
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International
Standard
ISO/IEC 16466
First edition
Information technology —
2025-08
3D printing and scanning —
Assessment methods of 3D scanned
data use in 3D printing
Reference number
© ISO/IEC 2025
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on
the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address below
or ISO’s member body in the country of the requester.
ISO copyright office
CP 401 • Ch. de Blandonnet 8
CH-1214 Vernier, Geneva
Phone: +41 22 749 01 11
Email: copyright@iso.org
Website: www.iso.org
Published in Switzerland
© ISO/IEC 2025 – All rights reserved
ii
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms, definitions and abbreviated terms . 1
3.1 Terms and definitions .1
3.2 Abbreviated terms .2
4 Assessment . . 3
4.1 General .3
4.1.1 Background .3
4.1.2 Published standards and metrics of assessment.4
4.2 Types of errors in image segmentation of 3D scanned data .10
4.2.1 General .10
4.2.2 Typical examples of each type of error .10
5 Approach to assessments . 10
5.1 General .10
5.2 Region of Intertest/Volume of Interest .11
5.3 Image enhancement and image normalization .11
5.4 Surface modelling and pre-processing (smoothing and averaging) .11
6 Assessment for segmentation of 2D images.11
6.1 General .11
6.2 Workflow and product quality .11
6.3 Assessment methods for image-based modelling/segmentation phase . . 12
6.3.1 Region-based measure/spatial overlap based metrics; Sensitivity, Specificity,
False-positive rate, False negative rate FNR, F-Measure FMS, Dice similarity
coefficient, Jaccard index . 12
6.3.2 Volume-based measure; Volume similarity, Volume overlap error, Volume
difference . 13
6.3.3 Probabilistic Distances Between Segmentation/Cross-correlation matrix
measure; Interclass correlation (ICC), AUC, Cohen kappa coefficient . 13
6.3.4 Distance-based measure/Spatial distance-based metrics; Hausdorff distance
(HD, Maximum Surface Distance), Mahalanobis distance . 13
7 Assessment for 3D modelled images .13
7.1 General . 13
7.2 Surface Distance-based Measure: Hausdorff distance, Average distance, Mean Absolute
Surface Distance, Mahalanobis distance .14
8 Choosing the most suitable metric for assessing image segmentation . 14
Annex A (informative) Preparation of Dataset for Assessment for image based modelling .15
Annex B (informative) Assessment for image based modelling of Craniofacial 3D images .16
Annex C (informative) Assessment for Orbital Segmentation . 19
Annex D (informative) Tools to evaluate the quality of image segmentation .22
Bibliography .24
© ISO/IEC 2025 – All rights reserved
iii
Foreword
ISO (the International Organization for Standardization) and IEC (the International Electrotechnical
Commission) form the specialized system for worldwide standardization. National bodies that are
members of ISO or IEC participate in the development of International Standards through technical
committees established by the respective organization to deal with particular fields of technical activity.
ISO and IEC technical committees collaborate in fields of mutual interest. Other international organizations,
governmental and non-governmental, in liaison with ISO and IEC, also take part in the work.
The procedures used to develop this document and those intended for its further maintenance are described
in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the different types
of document should be noted. This document was drafted in accordance with the editorial rules of the ISO/
IEC Directives, Part 2 (see www.iso.org/directives or www.iec.ch/members_experts/refdocs).
ISO and IEC draw attention to the possibility that the implementation of this document may involve the
use of (a) patent(s). ISO and IEC take no position concerning the evidence, validity or applicability of any
claimed patent rights in respect thereof. As of the date of publication of this document, ISO and IEC had not
received notice of (a) patent(s) which may be required to implement this document. However, implementers
are cautioned that this may not represent the latest information, which may be obtained from the patent
database available at www.iso.org/patents and https://patents.iec.ch. ISO and IEC shall not be held
responsible for identifying any or all such patent rights.
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and expressions
related to conformity assessment, as well as information about ISO's adherence to the World Trade
Organization (WTO) principles in the Technical Barriers to Trade (TBT) see www.iso.org/iso/foreword.html.
In the IEC, see www.iec.ch/understanding-standards.
This document was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology.
Any feedback or questions on this document should be directed to the user’s national standards
body. A complete listing of these bodies can be found at www.iso.org/members.html and
www.iec.ch/national-committees.
© ISO/IEC 2025 – All rights reserved
iv
Introduction
This document was developed in response to the need for quality management of 3D printing and scanning
technology through the use of information and communication technology (ICT).
3D scanning is the process of scanning a real-world object or environment to collect data on its shape and
possibly its style attributes. The main purpose of 3D scanning is for generating high-precision digital 3D models.
A 3D scanner can be based on many different technologies, each with its own purposes and targets,
limitations, and advantages. There could be many limitations in each type of target object that will be
digitized. For example, optical technology often encounters many difficulties with dark, shiny, reflective, or
transparent objects. Another example, as for computed tomography scanning, structured-light 3D scanners,
and LiDAR technology, the generation of digital 3D models requires the use of non-destructive internal
scanning technology.
Despite the rapid growth of 3D scanning applications, the accuracy, precision and reproducibility of
generated 3D models from 3D scanned data have not been thoroughly investigated. Especially if 3D scanned
data are used for 3D printing, their accuracy and precision are critical. Inaccuracies can arise due to errors
that occur during the imaging, segmentation, postprocessing, and 3D printing steps. The total accuracy,
precision, and reproducibility of 3D printed models are affected by the sum of errors introduced in each step
involved in the creation of the 3D models.
For the spreading of 3D printing applications, it is necessary to review and evaluate the various factors in
each step of the 3D model printing process that contribute to 3D model inaccuracy, including the intrinsic
limitations of each printing technology.
In this context, it is important to evaluate the overall process of data processing. In order to minimize
cumulative errors throughout the 3D printing life cycle using 3D scanned data, it is important to evaluate
and correct initial errors. By identifying and addressing these initial inaccuracies, the impact of errors
occurring during the 3D printing process can be greatly reduced. In addition, the method used to evaluate
3D scan data for 3D printing is also essential.
There are many algorithms for 3D scanned data such as semi-automatic segmentation, deformable model-
based segmentation, and Convolutional Neural Network based segmentation. There are several well-known
errors during image-based modelling of Region of Interest (ROI), which are over segmentation, under
segmentation, outlier, inaccurate contour, and malalignment. Even though there are more than twenty
metrics for evaluating 3D image segmentation, there is no consistent definition of metrics and suitable
combination of assessment metrics for 3D printing.
Segmentation assessment is the task of comparing two segmentations by measuring the distance or
similarity between them, where one is the segmentation to be assessed and the other is the corresponding
ground truth segmentation.
There are three major requirements (accuracy, precision, and efficiency) of assessment for 3D scanned
data. Accuracy is the degree to which the segmentation results agree with the ground truth segmentation.
Precision is a measure of repeatability. Efficiency is mostly related with time.
This document proposes assessment methods for 3D scanned data to evaluate and enhance the quality of 3D
printing models while minimizing errors.
© ISO/IEC 2025 – All rights reserved
v
International Standard ISO/IEC 16466:2025(en)
Information technology — 3D printing and scanning —
Assessment methods of 3D scanned data use in 3D printing
1 Scope
This document specifies methods and metrics for assessing the accuracy and precision of 3D scanned data
for use in 3D printing, throughout the
...
International
Standard
ISO/IEC 16466
First edition
Information technology —
3D printing and scanning —
Assessment methods of 3D scanned
data use in 3D printing
PROOF/ÉPREUVE
Reference number
© ISO/IEC 2025
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on
the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address below
or ISO’s member body in the country of the requester.
ISO copyright office
CP 401 • Ch. de Blandonnet 8
CH-1214 Vernier, Geneva
Phone: +41 22 749 01 11
Email: copyright@iso.org
Website: www.iso.org
Published in Switzerland
PROOF/ÉPREUVE
© ISO/IEC 2025 – All rights reserved
ii
Contents Page
Foreword .iv
Introduction .v
1 Scope . 1
2 Normative references . 1
3 Terms, definitions and abbreviated terms . 1
3.1 Terms and definitions .1
3.2 Abbreviated terms .2
4 Assessment . . 3
4.1 General .3
4.1.1 Background .3
4.1.2 Published standards and metrics of assessment.4
4.2 Types of errors in image segmentation of 3D scanned data .10
4.2.1 General .10
4.2.2 Typical examples of each type of error .10
5 Approach to assessments . 10
5.1 General .10
5.2 Region of Intertest/Volume of Interest .11
5.3 Image enhancement and image normalization .11
5.4 Surface modelling and pre-processing (smoothing and averaging) .11
6 Assessment for segmentation of 2D images.11
6.1 General .11
6.2 Workflow and product quality .11
6.3 Assessment methods for image-based modelling/segmentation phase . . 12
6.3.1 Region-based measure/spatial overlap based metrics; Sensitivity, Specificity,
False-positive rate, False negative rate FNR, F-Measure FMS, Dice similarity
coefficient, Jaccard index . 12
6.3.2 Volume-based measure; Volume similarity, Volume overlap error, Volume
difference . 13
6.3.3 Probabilistic Distances Between Segmentation/Cross-correlation matrix
measure; Interclass correlation (ICC), AUC, Cohen kappa coefficient . 13
6.3.4 Distance-based measure/Spatial distance-based metrics; Hausdorff distance
(HD, Maximum Surface Distance), Mahalanobis distance . 13
7 Assessment for 3D modelled images .13
7.1 General . 13
7.2 Surface Distance-based Measure: Hausdorff distance, Average distance, Mean Absolute
Surface Distance, Mahalanobis distance .14
8 Choosing the most suitable metric for assessing image segmentation . 14
Annex A (informative) Preparation of Dataset for Assessment for image based modelling .15
Annex B (informative) Assessment for image based modelling of Craniofacial 3D images .16
Annex C (informative) Assessment for Orbital Segmentation . 19
Annex D (informative) Tools to evaluate the quality of image segmentation .22
Bibliography .24
PROOF/ÉPREUVE
© ISO/IEC 2025 – All rights reserved
iii
Foreword
ISO (the International Organization for Standardization) and IEC (the International Electrotechnical
Commission) form the specialized system for worldwide standardization. National bodies that are
members of ISO or IEC participate in the development of International Standards through technical
committees established by the respective organization to deal with particular fields of technical activity.
ISO and IEC technical committees collaborate in fields of mutual interest. Other international organizations,
governmental and non-governmental, in liaison with ISO and IEC, also take part in the work.
The procedures used to develop this document and those intended for its further maintenance are described
in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the different types
of document should be noted. This document was drafted in accordance with the editorial rules of the ISO/
IEC Directives, Part 2 (see www.iso.org/directives or www.iec.ch/members_experts/refdocs).
ISO and IEC draw attention to the possibility that the implementation of this document may involve the
use of (a) patent(s). ISO and IEC take no position concerning the evidence, validity or applicability of any
claimed patent rights in respect thereof. As of the date of publication of this document, ISO and IEC had not
received notice of (a) patent(s) which may be required to implement this document. However, implementers
are cautioned that this may not represent the latest information, which may be obtained from the patent
database available at www.iso.org/patents and https://patents.iec.ch. ISO and IEC shall not be held
responsible for identifying any or all such patent rights.
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and expressions
related to conformity assessment, as well as information about ISO's adherence to the World Trade
Organization (WTO) principles in the Technical Barriers to Trade (TBT) see www.iso.org/iso/foreword.html.
In the IEC, see www.iec.ch/understanding-standards.
This document was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology.
Any feedback or questions on this document should be directed to the user’s national standards
body. A complete listing of these bodies can be found at www.iso.org/members.html and
www.iec.ch/national-committees.
PROOF/ÉPREUVE
© ISO/IEC 2025 – All rights reserved
iv
Introduction
This document was developed in response to the need for quality management of 3D printing and scanning
technology through the use of information and communication technology (ICT).
3D scanning is the process of scanning a real-world object or environment to collect data on its shape and
possibly its style attributes. The main purpose of 3D scanning is for generating high-precision digital 3D models.
A 3D scanner can be based on many different technologies, each with its own purposes and targets,
limitations, and advantages. There could be many limitations in each type of target object that will be
digitized. For example, optical technology often encounters many difficulties with dark, shiny, reflective, or
transparent objects. Another example, as for computed tomography scanning, structured-light 3D scanners,
and LiDAR technology, the generation of digital 3D models requires the use of non-destructive internal
scanning technology.
Despite the rapid growth of 3D scanning applications, the accuracy, precision and reproducibility of
generated 3D models from 3D scanned data have not been thoroughly investigated. Especially if 3D scanned
data are used for 3D printing, their accuracy and precision are critical. Inaccuracies can arise due to errors
that occur during the imaging, segmentation, postprocessing, and 3D printing steps. The total accuracy,
precision, and reproducibility of 3D printed models are affected by the sum of errors introduced in each step
involved in the creation of the 3D models.
For the spreading of 3D printing applications, it is necessary to review and evaluate the various factors in
each step of the 3D model printing process that contribute to 3D model inaccuracy, including the intrinsic
limitations of each printing technology.
In this context, it is important to evaluate the overall process of data processing. In order to minimize
cumulative errors throughout the 3D printing life cycle using 3D scanned data, it is important to evaluate
and correct initial errors. By identifying and addressing these initial inaccuracies, the impact of errors
occurring during the 3D printing process can be greatly reduced. In addition, the method used to evaluate
3D scan data for 3D printing is also essential.
There are many algorithms for 3D scanned data such as semi-automatic segmentation, deformable model-
based segmentation, and Convolutional Neural Network based segmentation. There are several well-known
errors during image-based modelling of Region of Interest (ROI), which are over segmentation, under
segmentation, outlier, inaccurate contour, and malalignment. Even though there are more than twenty
metrics for evaluating 3D image segmentation, there is no consistent definition of metrics and suitable
combination of assessment metrics for 3D printing.
Segmentation assessment is the task of comparing two segmentations by measuring the distance or
similarity between them, where one is the segmentation to be assessed and the other is the corresponding
ground truth segmentation.
There are three major requirements (accuracy, precision, and efficiency) of assessment for 3D scanned
data. Accuracy is the degree to which the segmentation results agree with the ground truth segmentation.
Precision is a measure of repeatability. Efficiency is mostly related with time.
This document proposes assessment methods for 3D scanned data to evaluate and enhance the quality of 3D
printing models while minimizing errors.
PROOF/ÉPREUVE
© ISO/IEC 2025 – All rights reserved
v
International Standard ISO/IEC 16466:2025(en)
Information technology — 3D printing and scanning —
Assessment methods of 3D scanned data use in 3D printing
1 Scope
This document specifies methods and metrics for assessing the accuracy and precision of 3
...
ISO/IEC DISPRF 16466:2025(en)
ISO/IEC JTC 1
Secretariat: ANSI
Date: 2025-04-0406-24
Information technology — 3D printing and scanning — Assessment
methods of 3D scanned data use in 3D printing
FDIS stage
ISO/IEC DISPRF 16466:20242025(en)
© ISO/IEC 20242025
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication
may be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying,
or posting on the internet or an intranet, without prior written permission. Permission can be requested from either ISO
at the address below or ISO’s member body in the country of the requester.
ISO copyright office
CP 401 • Ch. de Blandonnet 8
CH-1214 Vernier, Geneva
Phone: + 41 22 749 01 11
Fax: +41 22 749 09 47
EmailE-mail: copyright@iso.org
Website: www.iso.org
Published in Switzerland
© ISO #### /IEC 2025 – All rights reserved
ii
ISO/IEC DISPRF 16466:20242025(en)
Contents
Foreword . iv
Introduction . v
1 Scope . 1
2 Normative references . 1
3 Terms, definitions and abbreviated terms . 1
3.1 Terms and definitions . 1
3.2 Abbreviated terms . 2
4 Assessment . 3
4.1 General. 3
4.2 Types of errors in image segmentation of 3D scanned data . 11
5 Approach to assessments . 12
5.1 General. 12
5.2 Region of Intertest/Volume of Interest . 13
5.3 Image enhancement and image normalization . 13
5.4 Surface modelling and pre-processing (smoothing and averaging) . 13
6 Assessment for segmentation of 2D images . 13
6.1 General. 13
6.2 Workflow and product quality . 13
6.3 Assessment methods for image-based modelling/segmentation phase . 14
7 Assessment for 3D modelled images . 16
7.1 General. 16
7.2 Surface Distance-based Measure: Hausdorff distance, Average distance, Mean Absolute
Surface Distance, Mahalanobis distance . 16
8 Choosing the most suitable metric for assessing image segmentation . 16
Annex A (informative) Preparation of Dataset for Assessment for image based modelling . 17
Annex B (informative) Assessment for image based modelling of Craniofacial 3D images . 19
Annex C (informative) Assessment for Orbital Segmentation . 23
Annex D (informative) Tools to evaluate the quality of image segmentation . 27
Bibliography . 29
iii
ISO/IEC DISPRF 16466:20242025(en)
Foreword
ISO (the International Organization for Standardization) and IEC (the International Electrotechnical
Commission) form the specialized system for worldwide standardization. National bodies that are members
of ISO or IEC participate in the development of International Standards through technical committees
established by the respective organization to deal with particular fields of technical activity. ISO and IEC
technical committees collaborate in fields of mutual interest. Other international organizations, governmental
and non-governmental, in liaison with ISO and IEC, also take part in the work.
The procedures used to develop this document and those intended for its further maintenance are described
in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the different types of
ISO documents document should be noted. This document was drafted in accordance with the editorial rules
of the ISO/IEC Directives, Part 2 (see www.iso.org/directives or www.iec.ch/members_experts/refdocs).
ISO and IEC draw attention to the possibility that the implementation of this document may involve the use of
(a) patent(s). ISO and IEC take no position concerning the evidence, validity or applicability of any claimed
patent rights in respect thereof. As of the date of publication of this document, ISO and IEC had not received
notice of (a) patent(s) which may be required to implement this document. However, implementers are
cautioned that this may not represent the latest information, which may be obtained from the patent database
available at www.iso.org/patents and https://patents.iec.ch. ISO and IEC shall not be held responsible for
identifying any or all such patent rights.
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and expressions
related to conformity assessment, as well as information about ISO's adherence to the World Trade
Organization (WTO) principles in the Technical Barriers to Trade (TBT) see
www.iso.org/iso/foreword.htmlwww.iso.org/iso/foreword.html. In the IEC, see www.iec.ch/understanding-
standardswww.iec.ch/understanding- standards. .
This document was prepared by Joint Technical Committee ISO/IEC JTC 1, Information technology.
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www.iso.org/members.html and www.iec.ch/national-
committeeswww.iso.org/members.html and www.iec.ch/national-committees.
© ISO #### /IEC 2025 – All rights reserved
iv
ISO/IEC DISPRF 16466:20242025(en)
Introduction
This document was developed in response to the need for quality management of 3D printing and scanning
technology through the use of information and communication technology (ICT).
3D scanning is the process of scanning a real-world object or environment to collect data on its shape and
possibly its style attributes. The main purpose of 3D scanning is for generating high-precision digital 3D
models.
A 3D scanner can be based on many different technologies, each with its own purposes and targets, limitations,
and advantages. There could be many limitations in each type of target object that will be digitized. For
example, optical technology often encounters many difficulties with dark, shiny, reflective, or transparent
objects. Another example, as for computed tomography scanning, structured-light 3D scanners, and LiDAR
technology, the generation of digital 3D models requires the use of non-destructive internal scanning
technology.
Despite the rapid growth of 3D scanning applications, the accuracy, precision and reproducibility of generated
3D models from 3D scanned data have not been thoroughly investigated. Especially if 3D scanned data are
used for 3D printing, their accuracy and precision are critical. Inaccuracies can arise due to errors that occur
during the imaging, segmentation, postprocessing, and 3D printing steps. The total accuracy, precision, and
reproducibility of 3D printed models are affected by the sum of errors introduced in each step involved in the
creation of the 3D models.
For the spreading of 3D printing applications, it is necessary to review and evaluate the various factors in each
step of the 3D model printing process that contribute to 3D model inaccuracy, including the intrinsic
limitations of each printing technology.
In this context, it is important to evaluate the overall process of data processing. In order to minimize
cumulative errors throughout the 3D printing life cycle using 3D scanned data, it is important to evaluate and
correct initial errors. By identifying and addressing these initial inaccuracies, the impact of errors occurring
during the 3D printing process can be greatly reduced. In addition, the method used to evaluate 3D scan data
for 3D printing is also essential.
There are many algorithms for 3D scanned data such as semi-automatic segmentation, deformable model-
based segmentation, and Convolutional Neural Network based segmentation. There are several well-known
errors during image-based modelling of Region of Interest (ROI), which are over segmentation, under
segmentation, outlier, inaccurate contour, and malalignment. Even though there are more than twenty metrics
for evaluating 3D image segmentation, there is no consistent definition of metrics and suitable combination of
assessment metrics for 3D printing.
Segmentation assessment is the task of comparing two segmentations by measuring the distance or similarity
between them, where one is the segmentation to be assessed and the other is the corresponding ground truth
segmentation.
There are three major requirements (accuracy, precision, and efficiency) of assessment for 3D scanned data.
Accuracy is the degree to which the segmentation results agree with the ground truth segmentation. Precision
is a measure of repeatability. Efficiency is mostly related with time.
This document proposes assessment methods for 3D scanned data to evaluate and enhance the quality of 3D
printing models while minimizing errors.
v
ISO/IEC DIS 16466:2024(en)
DRAFT International Standard
Information technology — 3D printing and scanning –— Assessment
methods of 3D scanned data for use in 3D printing
1 Scope
This document specifies methods and metrics for assessing the accuracy and precision of 3D scanned data for
use in 3D printing, throughout the full 3D printing lifecycle.
This document focuses mainly on 3D scanned data from computed tomography. Computed tomography can
acquire information concerning the internal structures, regional density, orientation and/or alignment of
scanning objects, as well as their shape and appearance.
This document is applicable to the assessment of image-based modelling, segmentation, and 3D models.
This document is not intended to assess the 3D printed product itself.
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content constitutes
requirements of this document. For dated references, only the edition cited applies. For undated references,
the latest edition of the referenced document (including any amendments) applies.
There are no normative references in this document.
3 Terms, definitions and abbreviated terms
For the purposes of this document, the following terms and definitions apply.
ISO and IEC maintain terminological databases for use in standardization at the following addresses:
— — ISO Online browsing platform: available at https://www.iso.org/obphttps://www.iso.org/obp
— — IEC Electropedia: available at https://www.electropedia.org/http://www.electropedia.org/
3.1 Terms and definitions
3.1.1 3.1.1
assessment
act of judging or deciding the amount, value, quality, or importance of something, or the judgment or decision
that is made
3.1.2 3.1.2
contour
shape of an anatomical structure or part of body, especially its surface or the shape formed by its outer edge
3.1.3 3.1.3
enhancement
process of improving the quality, amount, or strength of something
ISO/IEC DISPRF 16466:20242025(en)
3.1.4 3.1.4
Euclidean distance
length of a line segment between the two points in Euclidean space
3.1.5 3.1.5
normalization
changing of the values of numeric columns in the dataset to use a common scale, without distorting differences
in the ranges of values
3.1.6 3.1.6
reference image
image that is assumed to have perfect quality
3.1.7 3.1.7
region of interest
ROI
boundary of a specific object defined in the image
[SOURCE: ISO/IEC 3532-2:2024, 3.7]
3.1.8 3.1.8
segmentation
process of separating the objects of interest from their surroundings
Note 1 to entry: segmentation can be applicable to 2D, 3D, raster or vector data.
Note 2 to entry: segmentation method: terms and definition standardized by ISO [ISO 13322-1:2014(en)].
3.1.9 3.1.9
segmented image
separated image extracted from original image with ROI
3.1.10 3.1.10
ground truth
ground truth label
correct answer of the training set for segmentation based on
...
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